Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows76
Duplicate rows (%)0.6%
Total size in memory1.7 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

Dataset has 76 (0.6%) duplicate rowsDuplicates
Administrative is highly overall correlated with Administrative_DurationHigh correlation
Administrative_Duration is highly overall correlated with AdministrativeHigh correlation
Informational is highly overall correlated with Informational_DurationHigh correlation
Informational_Duration is highly overall correlated with InformationalHigh correlation
ProductRelated is highly overall correlated with ProductRelated_Duration and 1 other fieldsHigh correlation
ProductRelated_Duration is highly overall correlated with ProductRelatedHigh correlation
BounceRates is highly overall correlated with ExitRatesHigh correlation
ExitRates is highly overall correlated with ProductRelated and 1 other fieldsHigh correlation
VisitorType is highly imbalanced (59.9%)Imbalance
Administrative has 5768 (46.8%) zerosZeros
Administrative_Duration has 5903 (47.9%) zerosZeros
Informational has 9699 (78.7%) zerosZeros
Informational_Duration has 9925 (80.5%) zerosZeros
ProductRelated_Duration has 755 (6.1%) zerosZeros
BounceRates has 5518 (44.8%) zerosZeros
PageValues has 9600 (77.9%) zerosZeros
SpecialDay has 11079 (89.9%) zerosZeros

Reproduction

Analysis started2023-02-18 06:09:21.206833
Analysis finished2023-02-18 06:09:34.808875
Duration13.6 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

Administrative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3151663
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:34.853521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3217841
Coefficient of variation (CV)1.4347929
Kurtosis4.7011462
Mean2.3151663
Median Absolute Deviation (MAD)1
Skewness1.9603572
Sum28546
Variance11.03425
MonotonicityNot monotonic
2023-02-17T22:09:34.906765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
Other values (17) 557
 
4.5%
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 4
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
 
< 0.1%
18 12
0.1%
17 16
0.1%

Administrative_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3335
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.818611
Minimum0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:34.967451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.26637
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.77911
Coefficient of variation (CV)2.1873564
Kurtosis50.556739
Mean80.818611
Median Absolute Deviation (MAD)7.5
Skewness5.615719
Sum996493.47
Variance31250.853
MonotonicityNot monotonic
2023-02-17T22:09:35.026511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5903
47.9%
4 56
 
0.5%
5 53
 
0.4%
7 45
 
0.4%
11 42
 
0.3%
6 41
 
0.3%
14 37
 
0.3%
9 35
 
0.3%
15 33
 
0.3%
10 32
 
0.3%
Other values (3325) 6053
49.1%
ValueCountFrequency (%)
0 5903
47.9%
1.333333333 1
 
< 0.1%
2 15
 
0.1%
3 26
 
0.2%
3.5 4
 
< 0.1%
4 56
 
0.5%
4.333333333 1
 
< 0.1%
4.5 2
 
< 0.1%
4.75 1
 
< 0.1%
5 53
 
0.4%
ValueCountFrequency (%)
3398.75 1
< 0.1%
2720.5 1
< 0.1%
2657.318056 1
< 0.1%
2629.253968 1
< 0.1%
2407.42381 1
< 0.1%
2156.166667 1
< 0.1%
2137.112745 1
< 0.1%
2086.75 1
< 0.1%
2047.234848 1
< 0.1%
1951.279141 1
< 0.1%

Informational
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50356853
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.086939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2701564
Coefficient of variation (CV)2.522311
Kurtosis26.932266
Mean0.50356853
Median Absolute Deviation (MAD)0
Skewness4.0364638
Sum6209
Variance1.6132973
MonotonicityNot monotonic
2023-02-17T22:09:35.137665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
9 15
 
0.1%
8 14
 
0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
8 14
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 7
 
0.1%
9 15
0.1%
8 14
 
0.1%
7 36
0.3%

Informational_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1258
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.472398
Minimum0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.200102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.74929
Coefficient of variation (CV)4.0829563
Kurtosis76.316853
Mean34.472398
Median Absolute Deviation (MAD)0
Skewness7.5791847
Sum425044.67
Variance19810.364
MonotonicityNot monotonic
2023-02-17T22:09:35.270272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9925
80.5%
9 33
 
0.3%
7 26
 
0.2%
10 26
 
0.2%
6 26
 
0.2%
12 23
 
0.2%
13 23
 
0.2%
16 22
 
0.2%
8 22
 
0.2%
11 21
 
0.2%
Other values (1248) 2183
 
17.7%
ValueCountFrequency (%)
0 9925
80.5%
1 3
 
< 0.1%
1.5 1
 
< 0.1%
2 11
 
0.1%
2.5 1
 
< 0.1%
3 16
 
0.1%
3.5 1
 
< 0.1%
4 17
 
0.1%
5 18
 
0.1%
5.5 3
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2195.3 1
< 0.1%
2166.5 1
< 0.1%
2050.433333 1
< 0.1%
1949.166667 1
< 0.1%
1830.5 1
< 0.1%
1779.166667 1
< 0.1%
1778 1
< 0.1%

ProductRelated
Real number (ℝ)

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.731468
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.587875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.475503
Coefficient of variation (CV)1.4016214
Kurtosis31.211707
Mean31.731468
Median Absolute Deviation (MAD)13
Skewness4.3415164
Sum391249
Variance1978.0704
MonotonicityNot monotonic
2023-02-17T22:09:35.647994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 622
 
5.0%
2 465
 
3.8%
3 458
 
3.7%
4 404
 
3.3%
6 396
 
3.2%
7 391
 
3.2%
5 382
 
3.1%
8 370
 
3.0%
10 330
 
2.7%
9 317
 
2.6%
Other values (301) 8195
66.5%
ValueCountFrequency (%)
0 38
 
0.3%
1 622
5.0%
2 465
3.8%
3 458
3.7%
4 404
3.3%
5 382
3.1%
6 396
3.2%
7 391
3.2%
8 370
3.0%
9 317
2.6%
ValueCountFrequency (%)
705 1
< 0.1%
686 1
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%
449 1
< 0.1%

ProductRelated_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9551
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.7462
Minimum0
Maximum63973.522
Zeros755
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.711784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369
Q31464.1572
95-th percentile4300.2891
Maximum63973.522
Range63973.522
Interquartile range (IQR)1280.0197

Descriptive statistics

Standard deviation1913.6693
Coefficient of variation (CV)1.6017371
Kurtosis137.17416
Mean1194.7462
Median Absolute Deviation (MAD)500.9369
Skewness7.2632277
Sum14731221
Variance3662130.1
MonotonicityNot monotonic
2023-02-17T22:09:35.770219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 755
 
6.1%
17 21
 
0.2%
11 17
 
0.1%
8 17
 
0.1%
15 16
 
0.1%
12 15
 
0.1%
19 15
 
0.1%
22 15
 
0.1%
13 14
 
0.1%
7 14
 
0.1%
Other values (9541) 11431
92.7%
ValueCountFrequency (%)
0 755
6.1%
0.5 1
 
< 0.1%
1 2
 
< 0.1%
2.333333333 1
 
< 0.1%
2.666666667 1
 
< 0.1%
3 5
 
< 0.1%
4 10
 
0.1%
5 13
 
0.1%
5.333333333 1
 
< 0.1%
6 5
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
29970.46597 1
< 0.1%
27009.85943 1
< 0.1%
24844.1562 1
< 0.1%
23888.81 1
< 0.1%
23342.08205 1
< 0.1%
23050.10414 1
< 0.1%
21857.04648 1
< 0.1%
21672.24425 1
< 0.1%

BounceRates
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1872
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138
Minimum0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.834284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.016812558
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.016812558

Descriptive statistics

Standard deviation0.048488322
Coefficient of variation (CV)2.185007
Kurtosis7.7231594
Mean0.02219138
Median Absolute Deviation (MAD)0.0031124675
Skewness2.9478553
Sum273.61972
Variance0.0023511174
MonotonicityNot monotonic
2023-02-17T22:09:35.898118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5518
44.8%
0.2 700
 
5.7%
0.066666667 134
 
1.1%
0.028571429 115
 
0.9%
0.05 113
 
0.9%
0.033333333 101
 
0.8%
0.025 100
 
0.8%
0.016666667 99
 
0.8%
0.1 98
 
0.8%
0.04 96
 
0.8%
Other values (1862) 5256
42.6%
ValueCountFrequency (%)
0 5518
44.8%
2.73 × 10-51
 
< 0.1%
3.35 × 10-51
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.2 700
5.7%
0.183333333 1
 
< 0.1%
0.18 5
 
< 0.1%
0.176923077 1
 
< 0.1%
0.175 1
 
< 0.1%
0.166666667 4
 
< 0.1%
0.164285714 1
 
< 0.1%
0.164230769 1
 
< 0.1%
0.161904762 1
 
< 0.1%
0.16 3
 
< 0.1%

ExitRates
Real number (ℝ)

Distinct4777
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043072798
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:35.968406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.025156403
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.048596541
Coefficient of variation (CV)1.128242
Kurtosis4.0170346
Mean0.043072798
Median Absolute Deviation (MAD)0.01417258
Skewness2.148789
Sum531.0876
Variance0.0023616238
MonotonicityNot monotonic
2023-02-17T22:09:36.032151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 710
 
5.8%
0.1 338
 
2.7%
0.05 329
 
2.7%
0.033333333 291
 
2.4%
0.066666667 267
 
2.2%
0.025 224
 
1.8%
0.04 214
 
1.7%
0.016666667 181
 
1.5%
0.02 167
 
1.4%
0.022222222 152
 
1.2%
Other values (4767) 9457
76.7%
ValueCountFrequency (%)
0 76
0.6%
0.000175593 1
 
< 0.1%
0.000250438 1
 
< 0.1%
0.000262123 1
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 1
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 710
5.8%
0.192307692 1
 
< 0.1%
0.188888889 2
 
< 0.1%
0.186666667 4
 
< 0.1%
0.183333333 2
 
< 0.1%
0.181818182 1
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 3
 
< 0.1%
0.177777778 5
 
< 0.1%
0.175 6
 
< 0.1%

PageValues
Real number (ℝ)

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8892579
Minimum0
Maximum361.76374
Zeros9600
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.099664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.160528
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.568437
Coefficient of variation (CV)3.1529332
Kurtosis65.635694
Mean5.8892579
Median Absolute Deviation (MAD)0
Skewness6.3829642
Sum72614.549
Variance344.78684
MonotonicityNot monotonic
2023-02-17T22:09:36.160535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9600
77.9%
53.988 6
 
< 0.1%
42.29306752 3
 
< 0.1%
59.988 2
 
< 0.1%
16.1585582 2
 
< 0.1%
44.89345937 2
 
< 0.1%
14.1273698 2
 
< 0.1%
34.03997536 2
 
< 0.1%
10.99901844 2
 
< 0.1%
58.9241766 2
 
< 0.1%
Other values (2694) 2707
 
22.0%
ValueCountFrequency (%)
0 9600
77.9%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.131837013 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
360.9533839 1
< 0.1%
287.9537928 1
< 0.1%
270.7846931 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 1
< 0.1%
255.5691579 1
< 0.1%
254.6071579 1
< 0.1%
246.7585902 1
< 0.1%
239.98 1
< 0.1%

SpecialDay
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061427413
Minimum0
Maximum1
Zeros11079
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.213880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19891727
Coefficient of variation (CV)3.2382492
Kurtosis9.9136589
Mean0.061427413
Median Absolute Deviation (MAD)0
Skewness3.3026667
Sum757.4
Variance0.039568082
MonotonicityNot monotonic
2023-02-17T22:09:36.256500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11079
89.9%
0.6 351
 
2.8%
0.8 325
 
2.6%
0.4 243
 
2.0%
0.2 178
 
1.4%
1 154
 
1.2%
ValueCountFrequency (%)
0 11079
89.9%
0.2 178
 
1.4%
0.4 243
 
2.0%
0.6 351
 
2.8%
0.8 325
 
2.6%
1 154
 
1.2%
ValueCountFrequency (%)
1 154
 
1.2%
0.8 325
 
2.6%
0.6 351
 
2.8%
0.4 243
 
2.0%
0.2 178
 
1.4%
0 11079
89.9%

Month
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.651987
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.303098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q311
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3928409
Coefficient of variation (CV)0.44339345
Kurtosis-1.6157458
Mean7.651987
Median Absolute Deviation (MAD)4
Skewness-0.055406923
Sum94349
Variance11.51137
MonotonicityNot monotonic
2023-02-17T22:09:36.347459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 3364
27.3%
11 2998
24.3%
3 1907
15.5%
12 1727
14.0%
10 549
 
4.5%
9 448
 
3.6%
8 433
 
3.5%
7 432
 
3.5%
6 288
 
2.3%
2 184
 
1.5%
ValueCountFrequency (%)
2 184
 
1.5%
3 1907
15.5%
5 3364
27.3%
6 288
 
2.3%
7 432
 
3.5%
8 433
 
3.5%
9 448
 
3.6%
10 549
 
4.5%
11 2998
24.3%
12 1727
14.0%
ValueCountFrequency (%)
12 1727
14.0%
11 2998
24.3%
10 549
 
4.5%
9 448
 
3.6%
8 433
 
3.5%
7 432
 
3.5%
6 288
 
2.3%
5 3364
27.3%
3 1907
15.5%
2 184
 
1.5%

OperatingSystems
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1240065
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.388606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91132483
Coefficient of variation (CV)0.42905934
Kurtosis10.456843
Mean2.1240065
Median Absolute Deviation (MAD)0
Skewness2.066285
Sum26189
Variance0.83051294
MonotonicityNot monotonic
2023-02-17T22:09:36.428126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6601
53.5%
1 2585
 
21.0%
3 2555
 
20.7%
4 478
 
3.9%
8 79
 
0.6%
6 19
 
0.2%
7 7
 
0.1%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 2585
 
21.0%
2 6601
53.5%
3 2555
 
20.7%
4 478
 
3.9%
5 6
 
< 0.1%
6 19
 
0.2%
7 7
 
0.1%
8 79
 
0.6%
ValueCountFrequency (%)
8 79
 
0.6%
7 7
 
0.1%
6 19
 
0.2%
5 6
 
< 0.1%
4 478
 
3.9%
3 2555
 
20.7%
2 6601
53.5%
1 2585
 
21.0%

Browser
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3570965
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.470340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7172767
Coefficient of variation (CV)0.72855594
Kurtosis12.746733
Mean2.3570965
Median Absolute Deviation (MAD)0
Skewness3.2423496
Sum29063
Variance2.9490392
MonotonicityNot monotonic
2023-02-17T22:09:36.510980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7961
64.6%
1 2462
 
20.0%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
10 163
 
1.3%
8 135
 
1.1%
3 105
 
0.9%
13 61
 
0.5%
7 49
 
0.4%
Other values (3) 17
 
0.1%
ValueCountFrequency (%)
1 2462
 
20.0%
2 7961
64.6%
3 105
 
0.9%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
7 49
 
0.4%
8 135
 
1.1%
9 1
 
< 0.1%
10 163
 
1.3%
ValueCountFrequency (%)
13 61
 
0.5%
12 10
 
0.1%
11 6
 
< 0.1%
10 163
 
1.3%
9 1
 
< 0.1%
8 135
 
1.1%
7 49
 
0.4%
6 174
 
1.4%
5 467
3.8%
4 736
6.0%

Region
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1473642
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.549999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4015912
Coefficient of variation (CV)0.76304842
Kurtosis-0.1486803
Mean3.1473642
Median Absolute Deviation (MAD)2
Skewness0.98354916
Sum38807
Variance5.7676405
MonotonicityNot monotonic
2023-02-17T22:09:36.588660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4780
38.8%
3 2403
19.5%
4 1182
 
9.6%
2 1136
 
9.2%
6 805
 
6.5%
7 761
 
6.2%
9 511
 
4.1%
8 434
 
3.5%
5 318
 
2.6%
ValueCountFrequency (%)
1 4780
38.8%
2 1136
 
9.2%
3 2403
19.5%
4 1182
 
9.6%
5 318
 
2.6%
6 805
 
6.5%
7 761
 
6.2%
8 434
 
3.5%
9 511
 
4.1%
ValueCountFrequency (%)
9 511
 
4.1%
8 434
 
3.5%
7 761
 
6.2%
6 805
 
6.5%
5 318
 
2.6%
4 1182
 
9.6%
3 2403
19.5%
2 1136
 
9.2%
1 4780
38.8%

TrafficType
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0695864
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-17T22:09:36.636305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0251692
Coefficient of variation (CV)0.98908557
Kurtosis3.4797106
Mean4.0695864
Median Absolute Deviation (MAD)1
Skewness1.9629867
Sum50178
Variance16.201987
MonotonicityNot monotonic
2023-02-17T22:09:36.679622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 3913
31.7%
1 2451
19.9%
3 2052
16.6%
4 1069
 
8.7%
13 738
 
6.0%
10 450
 
3.6%
6 444
 
3.6%
8 343
 
2.8%
5 260
 
2.1%
11 247
 
2.0%
Other values (10) 363
 
2.9%
ValueCountFrequency (%)
1 2451
19.9%
2 3913
31.7%
3 2052
16.6%
4 1069
 
8.7%
5 260
 
2.1%
6 444
 
3.6%
7 40
 
0.3%
8 343
 
2.8%
9 42
 
0.3%
10 450
 
3.6%
ValueCountFrequency (%)
20 198
 
1.6%
19 17
 
0.1%
18 10
 
0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
15 38
 
0.3%
14 13
 
0.1%
13 738
6.0%
12 1
 
< 0.1%
11 247
 
2.0%

VisitorType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
10551 
1
1694 
2
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Length

2023-02-17T22:09:36.730427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T22:09:36.789390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10551
85.6%
1 1694
 
13.7%
2 85
 
0.7%

Weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
9462 
1
2868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Length

2023-02-17T22:09:36.832339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T22:09:36.881583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Revenue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
10422 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Length

2023-02-17T22:09:36.921670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T22:09:36.966478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Interactions

2023-02-17T22:09:33.772140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:21.957724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.741225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.753844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.509699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.275824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.067992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.039780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.882094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.842052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.696846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.517890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.454178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.237973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.986749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.821589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.010356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.796465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.802165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.558147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.325277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.118787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.092719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.935888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.895711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.750749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.756027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.504767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.287878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.036092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.875127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.063367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.854924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.856908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.609794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.379716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.172989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.152344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.990909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.952005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.806817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.807809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.561765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.339935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.091068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.924739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.110907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.910483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.903845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.657896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.429954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.224980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.205636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.042581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.004745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.858521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.853864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.610952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.388736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.139753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.974368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.159338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.965914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.950941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.704790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.482785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.276178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.260267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.094102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.060350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.910917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.901238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.662414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.435102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.190594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.027841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.211392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.206828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.002820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.756543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.535417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.505529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.316094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.149948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.119089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.966850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.952147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.715163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.485594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.244010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.078093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.262672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.264812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.052518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.811633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.587380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.557782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.372698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.203289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.176113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.022213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.001592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.767784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.535848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.298246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.133526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.317292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.324428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.106279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.867171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.643103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.614407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.433506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.261010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.237049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.081206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.054514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.822480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.588757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.355614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.188884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.371469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.383172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.160256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.922406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.698645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.670823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.491535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.320391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.301674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.139851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.107544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.877705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.641947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.410246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.242044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.422374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.437212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.210055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.974862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.750998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.723584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.549066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.385859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.359799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.197259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.157818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.930150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.692579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.463875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.291981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.470837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.488594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.258089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.023899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.802723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.774599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.604959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.535389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.418258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.248572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.204818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.979730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.740245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.514071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.343011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.518434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.539360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.304754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.071484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.852303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.824341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.659444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.599027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.471563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.300689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.250446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.028437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.788067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.563183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.396574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.572121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.594366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.356376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.123152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.908834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.877871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.717346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.662154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.530234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.356811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.301753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.081421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.838212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.615884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.446119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.628771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.644516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.403000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.170460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.958285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.927639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.770840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.721236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.581058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.407938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.348073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.129684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.884784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.665372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:34.500942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:22.685457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:23.699310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:24.457999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:25.223605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.013783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:26.983207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:27.826702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:28.782905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:29.638828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:30.464564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:31.400619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.184273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:32.935883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-17T22:09:33.718171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-17T22:09:37.011329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
Administrative1.0000.9410.3690.3630.4600.422-0.155-0.4340.328-0.1250.080-0.005-0.0120.009-0.0120.0860.0280.131
Administrative_Duration0.9411.0000.3570.3520.4300.414-0.164-0.4380.317-0.1320.081-0.007-0.0230.019-0.0150.0070.0000.064
Informational0.3690.3571.0000.9510.3690.3680.006-0.1860.219-0.0540.0590.000-0.020-0.023-0.0290.0280.0110.078
Informational_Duration0.3630.3520.9511.0000.3610.363-0.002-0.2000.224-0.0540.0510.003-0.013-0.015-0.0260.0080.0000.068
ProductRelated0.4600.4300.3690.3611.0000.883-0.052-0.5190.342-0.0220.1400.0210.044-0.021-0.0700.0790.0000.127
ProductRelated_Duration0.4220.4140.3680.3630.8831.000-0.080-0.4770.360-0.0500.1340.0230.046-0.010-0.0730.0350.0040.072
BounceRates-0.155-0.1640.006-0.002-0.052-0.0801.0000.602-0.1240.135-0.0030.053-0.047-0.0180.0160.1230.0500.170
ExitRates-0.434-0.438-0.186-0.200-0.519-0.4770.6021.000-0.3080.151-0.0660.022-0.016-0.0040.0220.1840.0650.245
PageValues0.3280.3170.2190.2240.3420.360-0.124-0.3081.000-0.0700.062-0.0120.0260.001-0.0180.1100.0310.413
SpecialDay-0.125-0.132-0.054-0.054-0.022-0.0500.1350.151-0.0701.000-0.2520.0230.021-0.0150.1100.0640.2590.086
Month0.0800.0810.0590.0510.1400.134-0.003-0.0660.062-0.2521.0000.005-0.0220.017-0.0180.1350.0520.173
OperatingSystems-0.005-0.0070.0000.0030.0210.0230.0530.022-0.0120.0230.0051.0000.3750.0270.0800.4650.1180.074
Browser-0.012-0.023-0.020-0.0130.0440.046-0.047-0.0160.0260.021-0.0220.3751.0000.0550.0000.4720.0590.038
Region0.0090.019-0.023-0.015-0.021-0.010-0.018-0.0040.001-0.0150.0170.0270.0551.000-0.0040.1800.0170.010
TrafficType-0.012-0.015-0.029-0.026-0.070-0.0730.0160.022-0.0180.110-0.0180.0800.000-0.0041.0000.3160.0920.121
VisitorType0.0860.0070.0280.0080.0790.0350.1230.1840.1100.0640.1350.4650.4720.1800.3161.0000.0540.104
Weekend0.0280.0000.0110.0000.0000.0040.0500.0650.0310.2590.0520.1180.0590.0170.0920.0541.0000.028
Revenue0.1310.0640.0780.0680.1270.0720.1700.2450.4130.0860.1730.0740.0380.0100.1210.1040.0281.000

Missing values

2023-02-17T22:09:34.588683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-17T22:09:34.731609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.021111000
100.000.0264.0000000.0000000.1000000.00.022212000
200.000.010.0000000.2000000.2000000.00.024193000
300.000.022.6666670.0500000.1400000.00.023224000
400.000.010627.5000000.0200000.0500000.00.023314010
500.000.019154.2166670.0157890.0245610.00.022213000
600.000.010.0000000.2000000.2000000.00.422433000
710.000.000.0000000.2000000.2000000.00.021215010
800.000.0237.0000000.0000000.1000000.00.822223000
900.000.03738.0000000.0000000.0222220.00.422412000
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0112231000
1232100.0000.060.0000000.2000000.2000000.0000000.0111841000
12322676.2500.0221075.2500000.0000000.0041670.0000000.0122242000
12323264.7500.0441157.9761900.0000000.0139530.0000000.01122110000
1232400.0010.016503.0000000.0000000.0376470.0000000.0112211000
123253145.0000.0531783.7916670.0071430.02903112.2417170.0124611010
1232600.0000.05465.7500000.0000000.0213330.0000000.0113218010
1232700.0000.06184.2500000.0833330.0866670.0000000.01132113010
12328475.0000.015346.0000000.0000000.0210530.0000000.01122311000
1232900.0000.0321.2500000.0000000.0666670.0000000.0113212110

Duplicate rows

Most frequently occurring

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue# duplicates
1000.000.010.00.20.20.00.03221100014
2000.000.010.00.20.20.00.0332310007
2800.000.010.00.20.20.00.0522130007
2200.000.010.00.20.20.00.0511130006
6800.000.010.00.20.20.00.0128139202005
1800.000.010.00.20.20.00.0332110004
2500.000.010.00.20.20.00.0511430004
4600.000.010.00.20.20.00.01122110004
100.000.010.00.20.20.00.0232330003
700.000.010.00.20.20.00.0311330003